{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gv6nx80K4QNK"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "from keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iT0un9wb7QEb"
      },
      "source": [
        "Define constants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcFGFv2o7N1u"
      },
      "source": [
        "FAST_RUN = False\n",
        "IMAGE_WIDTH=128\n",
        "IMAGE_HEIGHT=128\n",
        "IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)\n",
        "IMAGE_CHANNELS=3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtsAalz-7TmB"
      },
      "source": [
        "Prepare data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_iDUIM77Vk4"
      },
      "source": [
        "Download data and save it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_05v3CbN7RpR"
      },
      "source": [
        "url = 'https://drive.google.com/file/d/1gjbSZV5NjjIVOqSP-yTCqXcGPX9PnYn5/view?usp=sharing'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fp1FQpyd7jwo",
        "outputId": "a2f054e4-98a8-4ea9-b419-00fecacb1243",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!gdown --id 1gjbSZV5NjjIVOqSP-yTCqXcGPX9PnYn5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1gjbSZV5NjjIVOqSP-yTCqXcGPX9PnYn5\n",
            "To: /content/MM17-WeiboRumorSet.zip\n",
            "1.35GB [00:18, 73.2MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hYPHGO1E7uh6"
      },
      "source": [
        "Unzip and save into folder train_extracted"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhviYA0i7spi"
      },
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile(\"MM17-WeiboRumorSet.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"./train_extracted\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWLQXyRB74iI"
      },
      "source": [
        "Exract labels from image names and save image names with corresponding labels in dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pk6D3XmT72dg"
      },
      "source": [
        "filenames1 = os.listdir(\"./train_extracted/MM17-WeiboRumorSet/rumor_images\")\n",
        "filenames01 = []\n",
        "categories1 = []\n",
        "for filename in filenames1:\n",
        "    category = 1 # rumor\n",
        "    categories1.append(1)\n",
        "    s = \"rumor_images/\"+str(filename)\n",
        "    filenames01.append(s)\n",
        "\n",
        "df1 = pd.DataFrame({\n",
        "    'filename': filenames01,\n",
        "    'category': categories1\n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHnbciSU8dYZ",
        "outputId": "877a709e-503e-45f1-db34-cd8e3b2dd32c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename  category\n",
              "0  rumor_images/005Cp0Zcjw1eqxg1ipc63j30hs0hztah.jpg         1\n",
              "1  rumor_images/720be4e5gw1eo6jz021qmj20c00e3mxq.jpg         1\n",
              "2  rumor_images/6a77ebb7jw1ev20xu2excj20p018gn18.jpg         1\n",
              "3  rumor_images/0062G6IYjw1er1zfiinvhj30wo1407az.jpg         1\n",
              "4  rumor_images/5d6ed51fjw1eb8s33w2yoj20c808q0u1.jpg         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9PJWG2A87dJ",
        "outputId": "987bdc3d-b1ef-4766-cf9a-09ce5bac9847",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(df1) # number of rumor images"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7954"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3x7rXsP8ebo"
      },
      "source": [
        "filenames2 = os.listdir(\"./train_extracted/MM17-WeiboRumorSet/nonrumor_images\")\n",
        "filenames02 = []\n",
        "categories2 = []\n",
        "for filename in filenames2:\n",
        "    category2 = 0 # rumor\n",
        "    categories2.append(0)\n",
        "    s = \"nonrumor_images/\"+str(filename)\n",
        "    filenames02.append(s)\n",
        "\n",
        "df2 = pd.DataFrame({\n",
        "    'filename': filenames02,\n",
        "    'category': categories2\n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SWw95p98wPA",
        "outputId": "c52654c3-ad42-4639-d472-8c9d0937069d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename  category\n",
              "0  nonrumor_images/61ff32dejw1ezpn9odko9j20c80f7m...         0\n",
              "1  nonrumor_images/66eeadffjw1eyz8i8z3e2j20hs0dcg...         0\n",
              "2  nonrumor_images/620beb06gw1eyl2duidwcj21e06k4h...         0\n",
              "3  nonrumor_images/61e6c012jw1ez1r01nnw5j20c81367...         0\n",
              "4  nonrumor_images/61b8c41ejw1evetlsoyaej20ic0c8t...         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d2VfS1D89Yr",
        "outputId": "32102034-386f-41e1-fe16-0bfb768146dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(df2) # number of non rumor images"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5318"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZ42s8Ro_6Va"
      },
      "source": [
        "df = df1.append(df2, ignore_index=True).reset_index(drop = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fIUquAhAZB-",
        "outputId": "900d2e0c-654d-4b92-d7e1-812f588bee1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>rumor_images/5d6ed51fjw1eb8s33w2yoj20c808q0u1.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename  category\n",
              "0  rumor_images/005Cp0Zcjw1eqxg1ipc63j30hs0hztah.jpg         1\n",
              "1  rumor_images/720be4e5gw1eo6jz021qmj20c00e3mxq.jpg         1\n",
              "2  rumor_images/6a77ebb7jw1ev20xu2excj20p018gn18.jpg         1\n",
              "3  rumor_images/0062G6IYjw1er1zfiinvhj30wo1407az.jpg         1\n",
              "4  rumor_images/5d6ed51fjw1eb8s33w2yoj20c808q0u1.jpg         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pH2wtpWAZ--",
        "outputId": "f1d65b64-cf4c-4421-9e73-bd7491885909",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(df) # combined"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13272"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
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      "metadata": {
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        "outputId": "d34aba52-59a6-4350-9faa-82efb4dbbade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13272.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.599307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.490057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           category\n",
              "count  13272.000000\n",
              "mean       0.599307\n",
              "std        0.490057\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        1.000000\n",
              "75%        1.000000\n",
              "max        1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEICoXREAe7W",
        "outputId": "5369cc8b-09da-49f3-9b99-1393d8681ad8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>nonrumor_images/66eeadffjw1eynry0u92vj20m80es7...</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>13271</th>\n",
              "      <td>nonrumor_images/0065yqVkjw1ezwqa63fg9j30fa0bgj...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                filename  category\n",
              "13267  nonrumor_images/66eeadffjw1eynry0u92vj20m80es7...         0\n",
              "13268  nonrumor_images/593bbc68gw1ezuivqza5aj20c80cbm...         0\n",
              "13269  nonrumor_images/6298156bgw1ey7b8tsjeyj20c6099w...         0\n",
              "13270  nonrumor_images/70e11e0fjw1eww3qmvoiaj20hi0atg...         0\n",
              "13271  nonrumor_images/0065yqVkjw1ezwqa63fg9j30fa0bgj...         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pm3W4A0BBFex"
      },
      "source": [
        "Total count"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwsJEYNoAnad",
        "outputId": "b2598e34-98b2-4f04-8a59-ff0f5ab9d606",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "label_counts = df.groupby('category').size()\n",
        "print(label_counts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "category\n",
            "0    5318\n",
            "1    7954\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hr5bn68EA5zF",
        "outputId": "48d781c3-8b97-4d52-da30-d1da66275be5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "df['category'].value_counts().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd98897c748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8SOMjuFBIZC"
      },
      "source": [
        "Sample image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zu95Vqf5ICnG"
      },
      "source": [
        "Shuffle df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOkhf8xkIEQm"
      },
      "source": [
        "# shuffle the DataFrame rows \n",
        "df = df.sample(frac = 1).reset_index(drop=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wz6egnsyIO6G",
        "outputId": "98357f9f-84d2-4b3b-f885-0718927c8a53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>rumor_images/68422e31jw1e0x1h1kxntj.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>rumor_images/7f77194agw1dw92geze18j.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nonrumor_images/61b8c41egw1etxnr0udzmj20rs0rs4...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rumor_images/7fcc7edetw1ed06g2cc90j20fc0aowfq.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>rumor_images/6394d19djw1egxi72x2zpj20m80euwmy.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename  category\n",
              "0            rumor_images/68422e31jw1e0x1h1kxntj.jpg         1\n",
              "1            rumor_images/7f77194agw1dw92geze18j.jpg         1\n",
              "2  nonrumor_images/61b8c41egw1etxnr0udzmj20rs0rs4...         0\n",
              "3  rumor_images/7fcc7edetw1ed06g2cc90j20fc0aowfq.jpg         1\n",
              "4  rumor_images/6394d19djw1egxi72x2zpj20m80euwmy.jpg         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4QGsffWIUje",
        "outputId": "e29baa39-4046-4d49-d057-99cd567f7112",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13267</th>\n",
              "      <td>rumor_images/662d0a05jw1eefosrz1tpj20dw0cwwf8.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13268</th>\n",
              "      <td>rumor_images/68496585jw1eqz88tnrv0j20p118gtg5.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13269</th>\n",
              "      <td>rumor_images/9ab7f7e5gw1dvmup7ivbuj.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13270</th>\n",
              "      <td>rumor_images/62622610jw1e5nk9ll5ltj205i030749.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13271</th>\n",
              "      <td>rumor_images/829014e6jw1dznefvfsxzj.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                filename  category\n",
              "13267  rumor_images/662d0a05jw1eefosrz1tpj20dw0cwwf8.jpg         1\n",
              "13268  rumor_images/68496585jw1eqz88tnrv0j20p118gtg5.jpg         1\n",
              "13269            rumor_images/9ab7f7e5gw1dvmup7ivbuj.jpg         1\n",
              "13270  rumor_images/62622610jw1e5nk9ll5ltj205i030749.jpg         1\n",
              "13271            rumor_images/829014e6jw1dznefvfsxzj.jpg         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X28HYdciIS-O"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkVovi9qDrBp"
      },
      "source": [
        "Make a combined list with rumor and non rumor image paths last part"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpX-qX_yGWgx",
        "outputId": "8db58af0-fbb0-4dc9-b667-e6bdb5e7a97d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "filenames = []\n",
        "categories = []\n",
        "for i in range(0, len(df)):\n",
        "  filenames.append(str(df['filename'][i]))\n",
        "  categories.append((df['category'][i]))\n",
        "print(len(filenames))\n",
        "print(len(categories))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13272\n",
            "13272\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQT75R37DViw",
        "outputId": "40920f5e-fe80-49d7-c1aa-3d17dac88f08",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(filenames[0])\n",
        "print(categories[0])\n",
        "print(filenames[-1])\n",
        "print(categories[-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rumor_images/68422e31jw1e0x1h1kxntj.jpg\n",
            "1\n",
            "rumor_images/829014e6jw1dznefvfsxzj.jpg\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNMAZ1nfCzsl",
        "outputId": "b51fd3e1-d071-4497-f663-e1a66531f025",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "sample = random.choice(filenames)\n",
        "print(sample)\n",
        "image = load_img(\"./train_extracted/MM17-WeiboRumorSet/\"+sample)\n",
        "plt.imshow(image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nonrumor_images/655dd5f3gw1eyyb8vodk1j20c806waal.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd98837cfd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XiFNN94EAWH"
      },
      "source": [
        "Build Model\n",
        "\n",
        "\n",
        "Input Layer: It represent input image data. It will reshape image into single diminsion array. Example your image is 64x64 = 4096, it will convert to (4096,1) array.\n",
        "\n",
        "Conv Layer: This layer will extract features from image.\n",
        "\n",
        "Pooling Layer: This layerreduce the spatial volume of input image after convolution.\n",
        "\n",
        "Fully Connected Layer: It connect the network from a layer to another layer\n",
        "\n",
        "Output Layer: It is the predicted values layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEFX8V1lDf6d",
        "outputId": "1e9efb04-6346-40e6-c145-a9333dac9267",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(2, activation='softmax')) # 2 because we have 2 classes\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 126, 126, 32)      896       \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 126, 126, 32)      128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 63, 63, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 63, 63, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 61, 61, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 61, 61, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 128)       73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 28, 28, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               12845568  \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 1026      \n",
            "=================================================================\n",
            "Total params: 12,942,786\n",
            "Trainable params: 12,941,314\n",
            "Non-trainable params: 1,472\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GTsRkNZlEp9S"
      },
      "source": [
        "Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9olGx-1Elzn"
      },
      "source": [
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlLvxlhiEwcY"
      },
      "source": [
        "Early Stop\n",
        "\n",
        "To prevent over fitting we will stop the learning after 10 epochs and val_loss value not decreased"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1myMXGP7Et10"
      },
      "source": [
        "earlystop = EarlyStopping(patience=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAFClnwJE3VD"
      },
      "source": [
        "Learning Rate Reduction\n",
        "\n",
        "We will reduce the learning rate when then accuracy not increase for 2 steps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9iT9fy0TE1Yb"
      },
      "source": [
        "learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', \n",
        "                                            patience=2, \n",
        "                                            verbose=1, \n",
        "                                            factor=0.5, \n",
        "                                            min_lr=0.00001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30fgqmTjE6aT"
      },
      "source": [
        "callbacks = [earlystop, learning_rate_reduction]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ilc_GkWHFHXY"
      },
      "source": [
        "Prepare data\n",
        "\n",
        "Because we will use image genaretor with class_mode=\"categorical\". We need to convert column category into string. Then imagenerator will convert it one-hot encoding which is good for our classification.\n",
        "\n",
        "So we will convert 1 to rumor and 0 to nonrumor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4vPWpOwE9ss"
      },
      "source": [
        "df[\"category\"] = df[\"category\"].replace({0: 'nonrumor', 1: 'rumor'}) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYBab-iIIkHP",
        "outputId": "6b852336-35fd-437e-e122-c1f6ae66e53c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>rumor_images/68422e31jw1e0x1h1kxntj.jpg</td>\n",
              "      <td>rumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>rumor_images/7f77194agw1dw92geze18j.jpg</td>\n",
              "      <td>rumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nonrumor_images/61b8c41egw1etxnr0udzmj20rs0rs4...</td>\n",
              "      <td>nonrumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rumor_images/7fcc7edetw1ed06g2cc90j20fc0aowfq.jpg</td>\n",
              "      <td>rumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>rumor_images/6394d19djw1egxi72x2zpj20m80euwmy.jpg</td>\n",
              "      <td>rumor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename  category\n",
              "0            rumor_images/68422e31jw1e0x1h1kxntj.jpg     rumor\n",
              "1            rumor_images/7f77194agw1dw92geze18j.jpg     rumor\n",
              "2  nonrumor_images/61b8c41egw1etxnr0udzmj20rs0rs4...  nonrumor\n",
              "3  rumor_images/7fcc7edetw1ed06g2cc90j20fc0aowfq.jpg     rumor\n",
              "4  rumor_images/6394d19djw1egxi72x2zpj20m80euwmy.jpg     rumor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vGd-5OuJxGK"
      },
      "source": [
        "Divide into 80% train and 20% test, Note that we have shuffled earlier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHvSkqYpIlEC",
        "outputId": "24fdcc41-437f-4105-8cf7-b0d62b03b2b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "mid = int(len(df)*0.8)\n",
        "df_train = df[:mid]\n",
        "df_test = df[mid:]\n",
        "print(len(df_train))\n",
        "print(len(df_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10617\n",
            "2655\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdHhrULaJytp"
      },
      "source": [
        "train_df, validate_df = train_test_split(df_train, test_size=0.20, random_state=42)\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "validate_df = validate_df.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89DDqmY2lXn1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_vv7UUvJ06a",
        "outputId": "1a753b27-22a3-4690-d8df-3fc86c80de5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "train_df['category'].value_counts().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd988332cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CDH0NKskfAx8BfsHMazPMvDZ/Y8i+Jp1H+hMuyS1VtX7oPqSnS3I78Kaq+vHQvfTEM+WT73+SGPraG/0QeGToJnrjkf6Eax913wrcBzzKk9+943X6GlSSw4DPAlcx89oEoKr+dLCmOuCc/uS7EHg/cBNPzulLe4N/BK7A1+ZYGfqTb1dVbR26CWkOL6qqvxi6id44vTPhknyamU/kfoWn/gvt1TsaVJK/Ae7ima9NL9ncgwz9CZfks3OUq6r+aOzNSCOS3DlHuarKSzb3IENfkjrinP6Ea0f6z3hn90hfQ0ty2lz1qrpo3L30xNCffKM/mPJS4A+A/x2oF2nUG0eWXwocA1wHGPp7kNM7nUmyD/Cdqnrz0L1Io5KsBC6uquOH7mWS+Ync/qwDXjV0E9IcfgYcMnQTk87pnQmWJMDjwMMj5fuADw7TkfSkJF/hyfNN+wDrgUuG66gPTu9MuCTfr6rfHroP6enaV4TMegy4u6q2D9VPLzzSn3zXJnljVV0zdCPSrPZbzR+uqrcN3UtvDP3JdwTwviR3MzNn6heuaXBV9XiSJ5LsV1UPDd1PTwz9yXfc0A1I83gYuCnJNmYOSAC/ZXNPM/QnXFXdPXQP0jy+hL/gNnaeyJWkjnikL2kQSY4CPgy8mpksmj3f5Beu7UEe6UsaRJIfAH8OXMvM50kAqKr7B2uqAx7pSxrKQ1X1jaGb6I1H+pIGkeQ8YAUzJ3NHf0TlusGa6oChL2kQSa6co1xVdfTYm+mIoS9JHfFbNiUNIsl+ST6ZZKrd/jbJfkP3NekMfUlD2Qz8FDil3X4CzPWbzlpGTu9IGkSSG6rq0IVqWl4e6Usays+TvGV2pX1Y6+cD9tMFj/QlDSLJocAWYHYe/wFgY1XdOFxXk8/QlzSIJC8BTgZ+E1gJPMTMJZvnDNrYhPMTuZKGcinwIHAdsGPgXrrhkb6kQfhTnsPwRK6kofx3kt8ZuoneeKQvaRBJbgFeA9zJzHfv+FOeY2DoSxpEklfPVffX3vYsQ1+SOuKcviR1xNCXpI4Y+pLUEUNfkjry/0ErkixOLYzdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ehgi-u8KJ4oi",
        "outputId": "68fdca7a-fbb6-4d42-cd1f-188503d917ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "validate_df['category'].value_counts().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd9888b1940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSP4mXwoJ8Ph"
      },
      "source": [
        "total_train = train_df.shape[0]\n",
        "total_validate = validate_df.shape[0]\n",
        "batch_size=32"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYUi4feTKBcc"
      },
      "source": [
        "Traning Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_ojPtyjJ-qR",
        "outputId": "e6d1efbb-3583-451d-fb32-49919c23adab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    rescale=1./255,\n",
        "    shear_range=0.1,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1\n",
        ")\n",
        "\n",
        "train_generator = train_datagen.flow_from_dataframe(\n",
        "    train_df, \n",
        "    \"./train_extracted/MM17-WeiboRumorSet/\", \n",
        "    x_col='filename',\n",
        "    y_col='category',\n",
        "    target_size=IMAGE_SIZE,\n",
        "    class_mode='categorical',\n",
        "    batch_size=batch_size\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8482 validated image filenames belonging to 2 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/dataframe_iterator.py:282: UserWarning: Found 11 invalid image filename(s) in x_col=\"filename\". These filename(s) will be ignored.\n",
            "  .format(n_invalid, x_col)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llVLhuxYKh2N"
      },
      "source": [
        "Validation Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijwdyzmvKc4Y",
        "outputId": "17547e77-1aeb-48a7-e989-00ffeb2ea895",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        "validation_generator = validation_datagen.flow_from_dataframe(\n",
        "    validate_df, \n",
        "    \"./train_extracted/MM17-WeiboRumorSet/\", \n",
        "    x_col='filename',\n",
        "    y_col='category',\n",
        "    target_size=IMAGE_SIZE,\n",
        "    class_mode='categorical',\n",
        "    batch_size=batch_size\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2118 validated image filenames belonging to 2 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/dataframe_iterator.py:282: UserWarning: Found 6 invalid image filename(s) in x_col=\"filename\". These filename(s) will be ignored.\n",
            "  .format(n_invalid, x_col)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVVVotrcK2P7"
      },
      "source": [
        "See how our generator work"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtfGbdKXKs_B",
        "outputId": "dc07cc52-690e-4426-aaef-9a13e9c9100e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "example_df = train_df.sample(n=1).reset_index(drop=True)\n",
        "example_generator = train_datagen.flow_from_dataframe(\n",
        "    example_df, \n",
        "    \"./train_extracted/MM17-WeiboRumorSet/\", \n",
        "    x_col='filename',\n",
        "    y_col='category',\n",
        "    target_size=IMAGE_SIZE,\n",
        "    class_mode='categorical'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1 validated image filenames belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4bJtNoKLQNf"
      },
      "source": [
        "Fit Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJW0H0FJOFoG"
      },
      "source": [
        "FAST_RUN = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYK7nnTtLE6y",
        "outputId": "3fdb9409-2e39-42cc-ee35-af55c33da688",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "epochs=3 if FAST_RUN else 20\n",
        "history = model.fit_generator(\n",
        "    train_generator, \n",
        "    epochs=epochs,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=total_validate//batch_size,\n",
        "    steps_per_epoch=total_train//batch_size,\n",
        "    callbacks=callbacks\n",
        ")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-41-28f6c98b1462>:8: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/20\n",
            "265/265 [==============================] - 96s 363ms/step - loss: 1.0231 - accuracy: 0.5511 - val_loss: 0.7146 - val_accuracy: 0.5043\n",
            "Epoch 2/20\n",
            "265/265 [==============================] - 96s 363ms/step - loss: 0.7370 - accuracy: 0.5807 - val_loss: 0.6836 - val_accuracy: 0.5743\n",
            "Epoch 3/20\n",
            "265/265 [==============================] - 95s 359ms/step - loss: 0.6765 - accuracy: 0.6017 - val_loss: 0.6479 - val_accuracy: 0.6122\n",
            "Epoch 4/20\n",
            "265/265 [==============================] - 94s 355ms/step - loss: 0.6595 - accuracy: 0.6137 - val_loss: 0.7034 - val_accuracy: 0.6165\n",
            "Epoch 5/20\n",
            "265/265 [==============================] - 95s 357ms/step - loss: 0.6404 - accuracy: 0.6247 - val_loss: 0.6526 - val_accuracy: 0.6188\n",
            "Epoch 6/20\n",
            "265/265 [==============================] - 95s 360ms/step - loss: 0.6288 - accuracy: 0.6368 - val_loss: 0.6095 - val_accuracy: 0.6809\n",
            "Epoch 7/20\n",
            "265/265 [==============================] - 95s 359ms/step - loss: 0.6272 - accuracy: 0.6433 - val_loss: 0.6653 - val_accuracy: 0.6312\n",
            "Epoch 8/20\n",
            "265/265 [==============================] - ETA: 0s - loss: 0.6242 - accuracy: 0.6459\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "265/265 [==============================] - 95s 360ms/step - loss: 0.6242 - accuracy: 0.6459 - val_loss: 0.8208 - val_accuracy: 0.5938\n",
            "Epoch 9/20\n",
            "265/265 [==============================] - 98s 369ms/step - loss: 0.5991 - accuracy: 0.6632 - val_loss: 0.5823 - val_accuracy: 0.6965\n",
            "Epoch 10/20\n",
            "265/265 [==============================] - 96s 362ms/step - loss: 0.5901 - accuracy: 0.6762 - val_loss: 0.6260 - val_accuracy: 0.6866\n",
            "Epoch 11/20\n",
            "265/265 [==============================] - 97s 367ms/step - loss: 0.5847 - accuracy: 0.6821 - val_loss: 0.5873 - val_accuracy: 0.6979\n",
            "Epoch 12/20\n",
            "265/265 [==============================] - 97s 366ms/step - loss: 0.5869 - accuracy: 0.6818 - val_loss: 0.5827 - val_accuracy: 0.7126\n",
            "Epoch 13/20\n",
            "265/265 [==============================] - 97s 365ms/step - loss: 0.5823 - accuracy: 0.6886 - val_loss: 0.5811 - val_accuracy: 0.7041\n",
            "Epoch 14/20\n",
            "265/265 [==============================] - ETA: 0s - loss: 0.5768 - accuracy: 0.6889\n",
            "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "265/265 [==============================] - 97s 365ms/step - loss: 0.5768 - accuracy: 0.6889 - val_loss: 0.6379 - val_accuracy: 0.6832\n",
            "Epoch 15/20\n",
            "265/265 [==============================] - 97s 366ms/step - loss: 0.5679 - accuracy: 0.6929 - val_loss: 0.5826 - val_accuracy: 0.7121\n",
            "Epoch 16/20\n",
            "265/265 [==============================] - ETA: 0s - loss: 0.5580 - accuracy: 0.7025\n",
            "Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "265/265 [==============================] - 96s 362ms/step - loss: 0.5580 - accuracy: 0.7025 - val_loss: 0.7587 - val_accuracy: 0.6534\n",
            "Epoch 17/20\n",
            "265/265 [==============================] - 99s 372ms/step - loss: 0.5583 - accuracy: 0.7105 - val_loss: 0.6094 - val_accuracy: 0.7135\n",
            "Epoch 18/20\n",
            "265/265 [==============================] - 98s 368ms/step - loss: 0.5549 - accuracy: 0.7050 - val_loss: 0.5958 - val_accuracy: 0.7112\n",
            "Epoch 19/20\n",
            "265/265 [==============================] - ETA: 0s - loss: 0.5511 - accuracy: 0.7111\n",
            "Epoch 00019: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "265/265 [==============================] - 97s 365ms/step - loss: 0.5511 - accuracy: 0.7111 - val_loss: 0.6122 - val_accuracy: 0.7074\n",
            "Epoch 20/20\n",
            "265/265 [==============================] - 97s 367ms/step - loss: 0.5471 - accuracy: 0.7146 - val_loss: 0.6327 - val_accuracy: 0.7017\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqmvDWKSO2-w"
      },
      "source": [
        "Save Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HUVXwMKO3gJ"
      },
      "source": [
        "model.save_weights(\"model.h5\")"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9gbIiI3VHfZ"
      },
      "source": [
        "model.save_weights(\"3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020Rumor_Nonrumor_50epochs_model.h5\")"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQw6YuLRq3tT"
      },
      "source": [
        "# saving the dataframe \n",
        "df.to_csv('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_5331.csv') "
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJ70WX4tq8s1"
      },
      "source": [
        "# saving the dataframe \n",
        "df_train.to_csv('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_train_5331.csv')\n",
        "# saving the dataframe \n",
        "df_test.to_csv('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_test_5331.csv') "
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3if-P8VVMLZ",
        "outputId": "ac3f144d-4cea-4ca7-de1b-cebf429f0d3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.download('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020Rumor_Nonrumor_50epochs_model.h5') "
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_4e010d8f-e4cc-40b4-8553-851235af46fa\", \"3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020Rumor_Nonrumor_50epochs_model.h5\", 51814192)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fabeuti9rJgv",
        "outputId": "dc5176f4-f210-40c7-fef3-235e644ea462",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "files.download('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_5331.csv') \n",
        "files.download('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_train_5331.csv') \n",
        "files.download('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_test_5331.csv') "
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_48652349-2c30-462f-92b6-020f183d84d4\", \"3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_5331.csv\", 830161)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_caa04477-d572-4029-85fb-549d3bd13390\", \"3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_train_5331.csv\", 661549)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_79109bd1-961d-489f-a389-84d4d9b5b854\", \"3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020df_test_5331.csv\", 168631)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtoywaWXkraL",
        "outputId": "48403e25-b1c5-4ddd-fc5d-21bfcf268a97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.load_weights(\"model.h5\")\n",
        "print(\"Loaded model from disk\")"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxt6QQ-hO8PG"
      },
      "source": [
        "Visualize Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NJQxiabO5h8",
        "outputId": "02ac7025-b94e-48cf-e9a7-d4ba7f60e65b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        }
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))\n",
        "ax1.plot(history.history['loss'], color='b', label=\"Training loss\")\n",
        "ax1.plot(history.history['val_loss'], color='r', label=\"validation loss\")\n",
        "ax1.set_xticks(np.arange(1, epochs, 1))\n",
        "ax1.set_yticks(np.arange(0, 1, 0.1))\n",
        "\n",
        "ax2.plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\n",
        "ax2.plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\n",
        "ax2.set_xticks(np.arange(1, epochs, 1))\n",
        "\n",
        "legend = plt.legend(loc='best', shadow=True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6oe8cu2PIPc"
      },
      "source": [
        "Prepare Testing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLANKo8ebfnZ",
        "outputId": "cb229d10-1b4c-4b81-ea60-0b50de5772d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "df_test.head()\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10617</th>\n",
              "      <td>nonrumor_images/655dd5f3gw1eyd77vhihoj20c8088d...</td>\n",
              "      <td>nonrumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10618</th>\n",
              "      <td>rumor_images/7cbc3a69jw1ebed7ezzebj20x718ggv6.jpg</td>\n",
              "      <td>rumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10619</th>\n",
              "      <td>nonrumor_images/6e05a6dagw1erwyo0axz0j20bl0gnw...</td>\n",
              "      <td>nonrumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10620</th>\n",
              "      <td>rumor_images/005NKAf4jw1emd26tazyfj30k10hs0t8.jpg</td>\n",
              "      <td>rumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10621</th>\n",
              "      <td>nonrumor_images/75b52ed2jw1ewc6iwf2sij20rs0iid...</td>\n",
              "      <td>nonrumor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                filename  category\n",
              "10617  nonrumor_images/655dd5f3gw1eyd77vhihoj20c8088d...  nonrumor\n",
              "10618  rumor_images/7cbc3a69jw1ebed7ezzebj20x718ggv6.jpg     rumor\n",
              "10619  nonrumor_images/6e05a6dagw1erwyo0axz0j20bl0gnw...  nonrumor\n",
              "10620  rumor_images/005NKAf4jw1emd26tazyfj30k10hs0t8.jpg     rumor\n",
              "10621  nonrumor_images/75b52ed2jw1ewc6iwf2sij20rs0iid...  nonrumor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UBNf5qQbtip"
      },
      "source": [
        "df_test = df_test.reset_index(drop = True)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgwp88LEPEJI",
        "outputId": "03bbc20f-9540-441d-c42e-060a275afea0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_filenames = []\n",
        "test_categories = []\n",
        "for i in range(0,len(df_test)):\n",
        "  test_filenames.append(df_test['filename'][i])\n",
        "  test_categories.append(df_test['category'][i])\n",
        "print(len(test_filenames))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2655\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oroYR9nFPKaX"
      },
      "source": [
        "test_df = df_test\n",
        "nb_samples = test_df.shape[0]"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ke2CJfEzeQxk"
      },
      "source": [
        "Create Testing Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Au0hQBuDPMi3",
        "outputId": "81e06a68-d437-46f9-f953-0945371abef6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_gen = ImageDataGenerator(rescale=1./255)\n",
        "test_generator = test_gen.flow_from_dataframe(\n",
        "    test_df, \n",
        "    \"./train_extracted/MM17-WeiboRumorSet/\", \n",
        "    x_col='filename',\n",
        "    y_col=None,\n",
        "    class_mode=None,\n",
        "    target_size=IMAGE_SIZE,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False,\n",
        "    validate_filenames=False\n",
        ")"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2655 non-validated image filenames.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QrM6f9seki3"
      },
      "source": [
        "Predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaL3pLJ2eT-x"
      },
      "source": [
        "predict = model.predict(test_generator, steps=np.ceil(nb_samples/batch_size))"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amQMkq5kemlx",
        "outputId": "5b9a27cf-c51a-401a-9278-330a852e004c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(predict)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.93478906 0.06521101]\n",
            " [0.30919182 0.6908082 ]\n",
            " [0.5775309  0.42246905]\n",
            " ...\n",
            " [0.63230324 0.3676967 ]\n",
            " [0.66922015 0.33077985]\n",
            " [0.502328   0.49767205]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCw8FByKeqYS"
      },
      "source": [
        "For categoral classication the prediction will come with probability of each category. So we will pick the category that have the highest probability with numpy average max"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfp83CZ6eoFA"
      },
      "source": [
        "test_df['category_predicted'] = np.argmax(predict, axis=-1)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBRiP8lvevMY"
      },
      "source": [
        "We will convert the predict category back into our generator classes by using train_generator.class_indices. It is the classes that image generator map while converting data into computer vision"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZ-sNkxResjR"
      },
      "source": [
        "label_map = dict((v,k) for k,v in train_generator.class_indices.items())\n",
        "test_df['category_predicted'] = test_df['category_predicted'].replace(label_map)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A3bZxpsKggzh"
      },
      "source": [
        "From our prepare data part. We map data with {1: 'rumor', 0: 'nonrumor'}. Now we will map the result back to rumor is 1 and nonrumor is 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0zaVoW2exJx"
      },
      "source": [
        "test_df['category_predicted'] = test_df['category_predicted'].replace({ 'rumor': 1, 'nonrumor': 0 })\n",
        "test_df['category'] = test_df['category'].replace({ 'rumor': 1, 'nonrumor': 0 })"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CpR5BXrngknH"
      },
      "source": [
        "Virtaulize Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3NO-gkEgiwd",
        "outputId": "9f4195f1-21d4-4ac7-b69b-737774f79d0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "test_df['category_predicted'].value_counts().plot.bar()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd9225e8550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIFDmAJpj36U",
        "outputId": "d6d3a652-b2a0-4a0a-9578-e853e534734b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "test_df['category'].value_counts().plot.bar()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd9224dd1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8Vgbm5Oj6re"
      },
      "source": [
        "y_pred = test_df['category_predicted']\n",
        "y_true = test_df['category']"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fl5UHyHCbf_M",
        "outputId": "fe247232-85e3-484a-fdaf-fdb18fca3518",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score # normal accuracy\n",
        "from sklearn.metrics import balanced_accuracy_score # used in case of imbalanced data sets, average of recall, from 0 to 1\n",
        "from sklearn.metrics import confusion_matrix # division of performance on the multilabels\n",
        "from sklearn.metrics import cohen_kappa_score # compares model against random prediction, from -1 to 1\n",
        "from sklearn.metrics import classification_report # for multilabel classification, gives precision, recall, f score, support, more\n",
        "target_names = ['class 0', 'class 1']\n",
        "\n",
        "def print_metrics(y_true, y_pred):\n",
        "    print(\"Accuracy:\", accuracy_score(y_true, y_pred))\n",
        "    print(\"Balanced Accuracy:\" , balanced_accuracy_score(y_true, y_pred))\n",
        "    print(\"Confusion Matrix:\\n\", confusion_matrix(y_true, y_pred))\n",
        "    print(\"Cohen Kappa Score:\", cohen_kappa_score(y_true, y_pred))\n",
        "    print(\"Classification Report:\\n\", classification_report(y_true, y_pred, target_names=target_names))\n",
        "    \n",
        "print('CNN Only')\n",
        "print_metrics(y_true, y_pred)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CNN Only\n",
            "Accuracy: 0.6998116760828625\n",
            "Balanced Accuracy: 0.7045729759118238\n",
            "Confusion Matrix:\n",
            " [[ 786  291]\n",
            " [ 506 1072]]\n",
            "Cohen Kappa Score: 0.39657834784401524\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     class 0       0.61      0.73      0.66      1077\n",
            "     class 1       0.79      0.68      0.73      1578\n",
            "\n",
            "    accuracy                           0.70      2655\n",
            "   macro avg       0.70      0.70      0.70      2655\n",
            "weighted avg       0.71      0.70      0.70      2655\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NninvD-ebAX3",
        "outputId": "07102c43-1873-405b-b0d2-0d57dc6f6d6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "# saving the dataframe of our results\n",
        "test_df.to_csv('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020test_df_5331.csv') \n",
        "files.download('3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020test_df_5331.csv')"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_51f50873-6fa7-455a-b03e-0feca8344295\", \"3_Conv_layers_1_COMP5331_Initial_Full_Classifier_Ayush_10102020test_df_5331.csv\", 156344)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuGiE__EpTAU"
      },
      "source": [
        ""
      ],
      "execution_count": 64,
      "outputs": []
    }
  ]
}